Quorumpeps database: chemical space, microbial origin and functionality of quorum sensing peptides

Molecular structural arrangement in quorum sensing and bacterial metabolic production

Quorum sensing (QS) regulates bacterial behaviors such as biofilm formation, virulence, and metabolite production through signaling molecules like acyl-homoserine lactones (AHLs), peptides, and AI-2. These signals are pivotal in bacterial communication, influencing pathogenicity and industrial applications. This review explores the molecular architecture of QS signals and their role in metabolite production, emphasizing structural modifications that disrupt bacterial communication to control virulence and enhance industrial processes. Key findings highlight the development of synthetic QS analogs, engineered inhibitors, and microbial consortia as innovative tools in biotechnology and medicine. The review underscores the potential of molecular engineering in managing microbial behaviors and optimizing applications like biofuel production, bioplastics, and anti-virulence therapies. Additionally, cross-species signaling mechanisms, particularly involving AI-2, reveal new opportunities for regulating interspecies cooperation and competition. This synthesis aims to bridge molecular insights with practical applications, showcasing how QS-based technologies can drive advancements in microbial biotechnology and therapeutic strategies.

Bioinspired peptide/polyamino acid assemblies as quorum sensing inhibitors for the treatment of bacterial infections

Insufficient development of new antibiotics and the rise in antimicrobial resistance are putting the world at risk of losing curative medicines against bacterial infection. Quorum sensing is a type of cellular signaling for cell-to-cell communication that plays critical roles in biofilm formation and antimicrobial resistance, and is expected to be a new type of effective target for drug resistant bacteria. In this review we highlight recent advances in bioinspired peptide/polyamino acid assemblies as quorum sensing inhibitors across various microbial communities. In addition, existing obstacles and future development directions of peptide/polyamino acid assemblies as quorum sensing inhibitors were proposed for broader clinical applications and translations. Overall, quorum sensing peptide/polyamino acid assemblies could be vital tools against bacterial infection and antimicrobial resistance.

Peptipedia v2.0: a peptide sequence database and user-friendly web platform. A major update

In recent years, peptides have gained significant relevance due to their therapeutic properties. The surge in peptide production and synthesis has generated vast amounts of data, enabling the creation of comprehensive databases and information repositories. Advances in sequencing techniques and artificial intelligence have further accelerated the design of tailor-made peptides. However, leveraging these techniques requires versatile and continuously updated storage systems, along with tools that facilitate peptide research and the implementation of machine learning for predictive systems. This work introduces Peptipedia v2.0, one of the most comprehensive public repositories of peptides, supporting biotechnological research by simplifying peptide study and annotation. Peptipedia v2.0 has expanded its collection by over 45% with peptide sequences that have reported biological activities. The functional biological activity tree has been revised and enhanced, incorporating new categories such as cosmetic and dermatological activities, molecular binding, and antiageing properties. Utilizing protein language models and machine learning, more than 90 binary classification models have been trained, validated, and incorporated into Peptipedia v2.0. These models exhibit average sensitivities and specificities of 0.877±0.0530 and 0.873±0.054, respectively, facilitating the annotation of more than 3.6 million peptide sequences with unknown biological activities, also registered in Peptipedia v2.0. Additionally, Peptipedia v2.0 introduces description tools based on structural and ontological properties and user-friendly machine learning tools to facilitate the application of machine learning strategies to study peptide sequences. Database URL: https://peptipedia.cl/.

Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization

Therapeutic peptides are therapeutic agents synthesized from natural amino acids, which can be used as carriers for precisely transporting drugs and can activate the immune system for preventing and treating various diseases. However, screening therapeutic peptides using biochemical assays is expensive, time-consuming, and limited by experimental conditions and biological samples, and there may be ethical considerations in the clinical stage. In contrast, screening therapeutic peptides using machine learning and computational methods is efficient, automated, and can accurately predict potential therapeutic peptides. In this study, a k-nearest neighbor model based on multi-Laplacian and kernel risk sensitive loss was proposed, which introduces a kernel risk loss function derived from the K-local hyperplane distance nearest neighbor model as well as combining the Laplacian regularization method to predict therapeutic peptides. The findings indicated that the suggested approach achieved satisfactory results and could effectively predict therapeutic peptide sequences.

BBSdb, an open resource for bacterial biofilm-associated proteins

Bacterial biofilms are organized heterogeneous assemblages of microbial cells encased within a self-produced matrix of exopolysaccharides, extracellular DNA and proteins. Over the last decade, more and more biofilm-associated proteins have been discovered and investigated. Furthermore, omics techniques such as transcriptomes, proteomes also play important roles in identifying new biofilm-associated genes or proteins. However, those important data have been uploaded separately to various databases, which creates obstacles for biofilm researchers to have a comprehensive access to these data. In this work, we constructed BBSdb, a state-of-the-art open resource of bacterial biofilm-associated protein. It includes 48 different bacteria species, 105 transcriptome datasets, 21 proteome datasets, 1205 experimental samples, 57,823 differentially expressed genes (DEGs), 13,605 differentially expressed proteins (DEPs), 1,930 'Top 5% differentially expressed genes', 444 'Threshold-based DEGs' and a predictor for prediction of biofilm-associated protein. In addition, 1,781 biofilm-associated proteins, including annotation and sequences, were extracted from 942 articles and public databases via text-mining analysis. We used E. coli as an example to represent how to explore potential biofilm-associated proteins in bacteria. We believe that this study will be of broad interest to researchers in field of bacteria, especially biofilms, which are involved in bacterial growth, pathogenicity, and drug resistance. Availability and implementation: The BBSdb is freely available at http://124.222.145.44/#!/.

Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides

With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.

Quorum sensing bacteria improve microbial networks stability and complexity in wastewater treatment plants

Quorum-sensing bacteria (QSB) are crucial factors for microbial communication, yet their ecological role in wastewater treatment plants (WWTPs) remains unclear. Here, we developed a method to identify QSB by comparing 16S rRNA gene sequences. QSB in 388 activated sludge samples collected from 130 WWTPs across China primarily were identified as rare taxa and conditionally rare taxa. A co-occurrence network shared by all sludge communities revealed that QSB exhibited higher average clustering coefficient (0.46) than non-QSB (0.15). Individual sludge networks demonstrated that quorum sensing microbiomes were positively correlated with network robustness and network complexity, including average clustering coefficient and link density. We confirmed that QSB keystones and QSB nodes have a positive impact on network complexity by influencing network modularity through a structural equation model. Meanwhile, QSB communities directly contributed to maintaining network robustness (r = 0.29, P < 0.05). Hence, QSB play an important role in promoting network complexity and stability. Furthermore, QSB communities were positively associated with the functional composition of activated sludge communities (r = 0.33, P < 0.01), especially the denitrification capacity (r = 0.45, P < 0.001). Overall, we elucidated the ecological significance of QSB and provided support for QS-based regulation of activated sludge microbial communities.

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Constructions of quorum sensing signaling network for activated sludge microbial community

In wastewater treatment systems, the interactions among various microbes based on chemical signals, namely quorum sensing (QS), play critical roles in influencing microbial structure and function. However, it is challenging to understand the QS-controlled behaviors and the underlying mechanisms in complex microbial communities. In this study, we constructed a QS signaling network, providing insights into the intra- and interspecies interactions of activated sludge microbial communities based on diverse QS signal molecules. Our research underscores the role of diffusible signal factors in both intra- and interspecies communication among activated sludge microorganisms, and signal molecules commonly considered to mediate intraspecies communication may also participate in interspecies communication. QS signaling molecules play an important role as communal resources among the entire microbial group. The communication network within the microbial community is highly redundant, significantly contributing to the stability of natural microbial systems. This work contributes to the establishment of QS signaling network for activated sludge microbial communities, which may complement metabolic exchanges in explaining activated sludge microbial community structure and may help with a variety of future applications, such as making the dynamics and resilience of highly complex ecosystems more predictable.

Bacterial quorum sensing orchestrates longitudinal interactions to shape microbiota assembly

BACKGROUND

The mechanism of microbiota assembly is one of the main problems in microbiome research, which is also the primary theoretical basis for precise manipulation of microbial communities. Bacterial quorum sensing (QS), as the most common means for bacteria to exchange information and interactions, is characterized by universality, specificity, and regulatory power, which therefore may influence the assembly processes of human microbiota. However, the regulating role of QS in microbiota assembly is rarely reported. In this study, we developed an optimized in vitro oral biofilm microbiota assembling (OBMA) model to simulate the time-series assembly of oral biofilm microbiota (OBM), by which to excavate the QS network and its regulating power in the process.

RESULTS

By using the optimized OBMA model, we were able to restore the assembly process of OBM and generate time-series OBM metagenomes of each day. We discovered a total of 2291 QS protein homologues related to 21 QS pathways. Most of these pathways were newly reported and sequentially enriched during OBM assembling. These QS pathways formed a comprehensive longitudinal QS network that included successively enriched QS hubs, such as Streptococcus, Veillonella-Megasphaera group, and Prevotella-Fusobacteria group, for information delivery. Bidirectional cross-talk among the QS hubs was found to play critical role in the directional turnover of microbiota structure, which in turn, influenced the assembly process. Subsequent QS-interfering experiments accurately predicted and experimentally verified the directional shaping power of the longitudinal QS network in the assembly process. As a result, the QS-interfered OBM exhibited delayed and fragile maturity with prolonged membership of Streptococcus and impeded membership of Prevotella and Fusobacterium.

CONCLUSION

Our results revealed an unprecedented longitudinal QS network during OBM assembly and experimentally verified its power in predicting and manipulating the assembling process. Our work provides a new perspective to uncover underlying mechanism in natural complex microbiota assembling and a theoretical basis for ultimately precisely manipulating human microbiota through intervention in the QS network. Video Abstract.

The leaderless communication peptide (LCP) class of quorum-sensing peptides is broadly distributed among Firmicutes

The human pathogen Streptococcus pyogenes secretes a short peptide (leaderless communication peptide, LCP) that mediates intercellular communication and controls bacterial virulence through interaction with its receptor, RopB. Here, we show that LCP and RopB homologues are present in other Firmicutes. We experimentally validate that LCPs with distinct peptide communication codes act as bacterial intercellular signals and regulate gene expression in Streptococcus salivarius, Streptococcus porcinus, Enterococcus malodoratus and Limosilactobacillus reuteri. Our results indicate that LCPs are more widespread than previously thought, and their characterization may uncover new signaling mechanisms and roles in coordinating diverse bacterial traits.

QSP: An open sequence database for quorum sensing related gene analysis with an automatic annotation pipeline

Quorum sensing (QS) has attracted great attention due to its important role in the bacterial interactions and its relevance to water management. With the development of high-throughput sequencing technology, a specific database for QS-related sequence annotation is urgently needed. Here, Hidden Markov Model (HMM) profiles for 38 types of QS-related proteins were built using a total of 4024 collected seed sequences. Based on both homolog search and keywords confirmation against the non-redundant database, we established a QS-related protein (QSP) database, that includes 809,721 protein sequences and 186,133 nucleotide sequences, downloaded available at: https://github.com/chunxiao-dcx/QSP. The entries were classified into 38 types and 315 subtypes among 91 bacterial phyla. Furthermore, an automatic annotation pipeline, named QSAP, was developed for rapid annotation, classification and abundance quantification of QSP-like sequences from sequencing data. This pipeline provided the two homolog alignment strategies offered by Diamond (Blastp) or HMMER (Hmmscan), as well as a data cleansing function for a subset or union set of the hits. The pipeline was tested using 14 metagenomic samples from various water environments, including activated sludge, deep-sea sediments, estuary water, and reservoir water. The QSAP pipeline is freely available for academic use in the code repository at: https://github.com/chunxiao-dcx/QSAP. The establishment of this database and pipeline, provides a useful tool for QS-related sequence annotation in a wide range of projects, and will increase our understanding of QS communication in aquatic environments.

Peptidomics: LC-MS operational parameters do matter

The sensitive and specific detection of peptides at low levels in biofluids is critical to increase the lab-to-human translation of peptidomic research. An interesting group of peptides with increasing evidence for involvement in human diseases are quorum sensing peptides. To obtain more reliable conclusions on peptide measurands in biofluids, a selection of often neglected parts of the analytical process using LC-MS were investigated, with novel approaches recommended for each part. Quorum sensing peptides were used as the main model-peptides. The peptidomic parts investigated and discussed here are: Our work addresses aQbD-approached solutions to these challenges, encompassing sample stabilization measures, a suitable peptide anti-adsorption tool, judicious choice of injection solvent versus gradient system and optimal duty cycle parameters. Our recommendations will improve the peptidomics bio-analytics of not only quorum sensing peptides, but can also be of value for other measurands at low concentrations.

Computational tools for exploring peptide-membrane interactions in gram-positive bacteria

The vital cellular functions in Gram-positive bacteria are controlled by signaling molecules known as quorum sensing peptides (QSPs), considered promising therapeutic interventions for bacterial infections. In the bacterial system QSPs bind to membrane-coupled receptors, which then auto-phosphorylate and activate intracellular response regulators. These response regulators induce target gene expression in bacteria. One of the most reliable trends in drug discovery research for virulence-associated molecular targets is the use of peptide drugs or new functionalities. In this perspective, computational methods act as auxiliary aids for biologists, where methodologies based on machine learning and in silico analysis are developed as suitable tools for target peptide identification. Therefore, the development of quick and reliable computational resources to identify or predict these QSPs along with their receptors and inhibitors is receiving considerable attention. The databases such as Quorumpeps and Quorum Sensing of Human Gut Microbes (QSHGM) provide a detailed overview of the structures and functions of QSPs. The tools and algorithms such as QSPpred, QSPred-FL, iQSP, EnsembleQS and PEPred-Suite have been used for the generic prediction of QSPs and feature representation. The availability of compiled key resources for utilizing peptide features based on amino acid composition, positional preferences, and motifs as well as structural and physicochemical properties, including biofilm inhibitory peptides, can aid in elucidating the QSP and membrane receptor interactions in infectious Gram-positive pathogens. Herein, we present a comprehensive survey of diverse computational approaches that are suitable for detecting QSPs and QS interference molecules. This review highlights the utility of these methods for developing potential biomarkers against infectious Gram-positive pathogens.

PSRQSP: An effective approach for the interpretable prediction of quorum sensing peptide using propensity score representation learning

Quorum sensing peptides (QSPs) are microbial signaling molecules involved in several cellular processes, such as cellular communication, virulence expression, bioluminescence, and swarming, in various bacterial species. Understanding QSPs is essential for identifying novel drug targets for controlling bacterial populations and pathogenicity. In this study, we present a novel computational approach (PSRQSP) for improving the prediction and analysis of QSPs. In PSRQSP, we develop a novel propensity score representation learning (PSR) scheme. Specifically, we utilized the PSR approach to extract and learn a comprehensive set of estimated propensities of 20 amino acids, 400 dipeptides, and 400 g-gap dipeptides from a pool of scoring card method-based models. Finally, to maximize the utility of the propensity scores, we explored a set of optimal propensity scores and combined them to construct a final meta-predictor. Our experimental results showed that combining multiview propensity scores was more beneficial for identifying QSPs than the conventional feature descriptors. Moreover, extensive benchmarking experiments based on the independent test were sufficient to demonstrate the predictive capability and effectiveness of PSRQSP by outperforming the conventional ML-based and existing methods, with an accuracy of 94.44% and AUC of 0.967. PSR-derived propensity scores were employed to determine the crucial physicochemical properties for a better understanding of the functional mechanisms of QSPs. Finally, we constructed an easy-to-use web server for the PSRQSP (http://pmlabstack.pythonanywhere.com/PSRQSP). PSRQSP is anticipated to be an efficient computational tool for accelerating the data-driven discovery of potential QSPs for drug discovery and development.

Development of a Growth-Dependent System to Regulate Cell Growth and Keratinase Production in B. subtilis

Keratinases specifically degrade insoluble keratin waste, thus contributing to environmental protection and sustainable biomass development. However, their industrial application is hindered by inefficient enzyme production and poor biomass generation. In this study, the heterologous expression of keratinase was found to have cytotoxicity and might block host cell growth due to its proteolytic property. To address this problem, an autoregulatory expression system based on quorum sensing was developed to synergistically regulate cell growth and keratinase production in Bacillus subtilis. The growth-dependent promoter P_aprE was chosen and shown to be effective in delaying keratinase production while promoting host cell proliferation. Copy number screening and core region mutations further balanced the two states. Carbon supplement optimization indicated that addition of 2% glucose facilitated biomass accumulation during the early stage of fermentation. Cell density increased to 15.6 (OD_600 nm) from 8 with keratinase activity raised to 4200 U·mL^-1 from 1162 U·mL^-1. Keratinase was then utilized in the bioconversion of feather waste to prepare soluble keratins, polypeptides, and amino acids. This study provides a powerful system for efficient production of keratinase and paves the way for keratin waste recycling.

Bacterial Quorum-Sensing Peptides as Immune Modulators Present in Systemic Circulation

Quorum-sensing peptides (QSPs) are bacterial peptides traditionally considered only as inter-bacterial communication molecules. Recently, their involvement in microbiome-host interactions influencing host diseases such as cancer and sarcopenia were explored. However, it is still unknown to what extent these peptides have the potential to modulate the immune system. In this proof-of-concept study, we screened 89 QSPs for their potential to induce IL-6 and TNFα in murine splenocytes and J774 macrophages. Confirmatory experiments on the positive screening-hits were conducted using murine splenocytes and human PBMCs of different ages. Finally, to investigate the biological relevance of immunomodulatory QSPs, we analysed plasma in a human cohort for the presence of the immunomodulatory QSP Q010. To do this, we used a newly developed UHPLC-MS/MS method. Our findings indicated that specific QSPs activate immune cells in vitro, with Q007, Q010, Q017 and Q212 being the top four screening hits. Q007 and Q010 were affirmed in subsequent confirmatory experiments using murine splenocytes and human PBMCs. Finally, Q010 was detected in human plasma, demonstrating for the first time the presence of an immunomodulatory QSP in human circulation. In conclusion, our data are the first evidence indicating the potential of biologically relevant quorum-sensing peptides to modulate the immune system.

Quorum sensing-based interactions among drugs, microbes, and diseases

Many diseases and health conditions are closely related to various microbes, which participate in complex interactions with diverse drugs; nonetheless, the detailed targets of such drugs remain to be elucidated. Many existing studies have reported causal associations among drugs, gut microbes, or diseases, calling for a workflow to reveal their intricate interactions. In this study, we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease (QS-DMD) database ( http://www.qsdmd.lbci.net/ ), which includes diverse interactions for more than 8,000 drugs, 163 microbes, and 42 common diseases. Potential interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental validations. Furthermore, we have constructed a QS-based drug-receptor interaction network, proposed a systematic framework including various drug-receptor-microbe-disease connections, and mapped a paradigmatic circular interaction network based on the QS-DMD, which can provide the underlying QS-based mechanisms for the reported causal associations. The QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse diseases. This work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.

Quorum sensing in human gut and food microbiomes: Significance and potential for therapeutic targeting

Human gut and food microbiomes interact during digestion. The outcome of these interactions influences the taxonomical composition and functional capacity of the resident human gut microbiome, with potential consequential impacts on health and disease. Microbe-microbe interactions between the resident and introduced microbiomes, which likely influence host colonisation, are orchestrated by environmental conditions, elements of the food matrix, host-associated factors as well as social cues from other microorganisms. Quorum sensing is one example of a social cue that allows bacterial communities to regulate genetic expression based on their respective population density and has emerged as an attractive target for therapeutic intervention. By interfering with bacterial quorum sensing, for instance, enzymatic degradation of signalling molecules (quorum quenching) or the application of quorum sensing inhibitory compounds, it may be possible to modulate the microbial composition of communities of interest without incurring negative effects associated with traditional antimicrobial approaches. In this review, we summarise and critically discuss the literature relating to quorum sensing from the perspective of the interactions between the food and human gut microbiome, providing a general overview of the current understanding of the prevalence and influence of quorum sensing in this context, and assessing the potential for therapeutic targeting of quorum sensing mechanisms.

Biofilm-i: A Platform for Predicting Biofilm Inhibitors Using Quantitative Structure-Relationship (QSAR) Based Regression Models to Curb Antibiotic Resistance

Antibiotic drug resistance has emerged as a major public health threat globally. One of the leading causes of drug resistance is the colonization of microorganisms in biofilm mode. Hence, there is an urgent need to design novel and highly effective biofilm inhibitors that can work either synergistically with antibiotics or individually. Therefore, we have developed a recursive regression-based platform “Biofilm-i” employing a quantitative structure–activity relationship approach for making generalized predictions, along with group and species-specific predictions of biofilm inhibition efficiency of chemical(s). The platform encompasses eight predictors, three analysis tools, and data visualization modules. The experimentally validated biofilm inhibitors for model development were retrieved from the “aBiofilm” resource and processed using a 10-fold cross-validation approach using the support vector machine and andom forest machine learning techniques. The data was further sub-divided into training/testing and independent validation sets. From training/testing data sets the Pearson’s correlation coefficient of overall chemicals, Gram-positive bacteria, Gram-negative bacteria, fungus, Pseudomonas aeruginosa, Staphylococcus aureus, Candida albicans, and Escherichia coli was 0.60, 0.77, 0.62, 0.77, 0.73, 0.83, 0.70, and 0.71 respectively via Support Vector Machine. Further, all the QSAR models performed equally well on independent validation data sets. Additionally, we also checked the performance of the random forest machine learning technique for the above datasets. The integrated analysis tools can convert the chemical structure into different formats, search for a similar chemical in the aBiofilm database and design the analogs. Moreover, the data visualization modules check the distribution of experimentally validated biofilm inhibitors according to their common scaffolds. The Biofilm-i platform would be of immense help to researchers engaged in designing highly efficacious biofilm inhibitors for tackling the menace of antibiotic drug resistance.

Rational Discovery of Antimicrobial Peptides by Means of Artificial Intelligence

Antibiotic resistance is a worldwide public health problem due to the costs and mortality rates it generates. However, the large pharmaceutical industries have stopped searching for new antibiotics because of their low profitability, given the rapid replacement rates imposed by the increasingly observed resistance acquired by microorganisms. Alternatively, antimicrobial peptides (AMPs) have emerged as potent molecules with a much lower rate of resistance generation. The discovery of these peptides is carried out through extensive in vitro screenings of either rational or non-rational libraries. These processes are tedious and expensive and generate only a few AMP candidates, most of which fail to show the required activity and physicochemical properties for practical applications. This work proposes implementing an artificial intelligence algorithm to reduce the required experimentation and increase the efficiency of high-activity AMP discovery. Our deep learning (DL) model, called AMPs-Net, outperforms the state-of-the-art method by 8.8% in average precision. Furthermore, it is highly accurate to predict the antibacterial and antiviral capacity of a large number of AMPs. Our search led to identifying two unreported antimicrobial motifs and two novel antimicrobial peptides related to them. Moreover, by coupling DL with molecular dynamics (MD) simulations, we were able to find a multifunctional peptide with promising therapeutic effects. Our work validates our previously proposed pipeline for a more efficient rational discovery of novel AMPs.

An insight into reactivity and bioactivity properties of quorum sensing peptides against PDE10A: a computational peptidology approach

Peptides are currently the most promising lead molecules. Quorum sensing peptides have a variety of structural features and are regularly exposed to post-translational modifications. Antiparkinsonian drugs lose their efficacy after a long period of use, and patients develop motor problems such as drug-induced dyskinesia (DIDs). The interaction between PDE10A and cAMP is necessary for dopamine neurotransmission and may play a role in Parkinson's disease pathogenesis. cAMP and cGMP are cyclic nucleotides that act as secondary messengers in the signal transduction pathway, influencing a range of CNS activities. PDE enzymes hydrolyze phosphodiester bonds to break down cAMP and cGMP, allowing them to control intracellular levels of these second messengers effectively. PDE expression, and hence cyclic nucleotide levels and their downstream targets, may change with age and in numerous age-related illnesses, including Parkinson's disease, according to mounting evidence. At the peak of dyskinesias, cyclic nucleotide levels were lower, and using phosphodiesterase inhibitors before antiparkinsonian medicines reduced the severity of dyskinesias. In a recent study, PapRIV was found to have the ability to activate BV-2 microglia cells, indicating that this quorum sensing peptide may play a role in gut-brain contact. As a result of the current in silico work, mainly focused on QSPs as a lead molecule for inhibiting PDE10A, the SRNAT QSP sequence has been a potent molecule in molecular docking and molecular dynamics simulations. Furthermore, we can test the efficiency of therapeutic components in vitro and in vivo utilizing this computational approach against PDE10A.

The quorum sensing peptide EntF* promotes colorectal cancer metastasis in mice: a new factor in the host-microbiome interaction

Background

Colorectal cancer, one of the most common malignancies worldwide, is associated with a high mortality rate, mainly caused by metastasis. Comparative metagenome-wide association analyses of healthy individuals and cancer patients suggest a role for the human intestinal microbiota in tumor progression. However, the microbial molecules involved in host-microbe communication are largely unknown, with current studies mainly focusing on short-chain fatty acids and amino acid metabolites as potential mediators. Quorum sensing peptides are not yet considered in this context since their presence in vivo and their ability to affect host cells have not been reported so far.

Results

Here, we show that EntF*, a metabolite of the quorum sensing peptide EntF produced by Enterococcus faecium, is naturally present in mice bloodstream. Moreover, by using an orthotopic mouse model, we show that EntF* promotes colorectal cancer metastasis in vivo, with metastatic lesions in liver and lung tissues. In vitro tests suggest that EntF* regulates E-cadherin expression and consequently the epithelial-mesenchymal transition, via the CXCR4 receptor. In addition, alanine-scanning analysis indicates that the first, second, sixth, and tenth amino acid of EntF* are critical for epithelial-mesenchymal transition and tumor metastasis.

Conclusion

Our work identifies a new class of molecules, quorum sensing peptides, as potential regulators of host-microbe interactions. We prove, for the first time, the presence of a selected quorum sensing peptide metabolite in a mouse model, and we demonstrate its effects on colorectal cancer metastasis. We believe that our work represents a starting point for future investigations on the role of microbiome in colorectal cancer metastasis and for the development of novel bio-therapeutics in other disease areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01317-z.

Machine learning aided construction of the quorum sensing communication network for human gut microbiota

Quorum sensing (QS) is a cell-cell communication mechanism that connects members in various microbial systems. Conventionally, a small number of QS entries are collected for specific microbes, which is far from being able to fully depict communication-based complex microbial interactions in human gut microbiota. In this study, we propose a systematic workflow including three modules and the use of machine learning-based classifiers to collect, expand, and mine the QS-related entries. Furthermore, we develop the Quorum Sensing of Human Gut Microbes (QSHGM) database ( http://www.qshgm.lbci.net/ ) including 28,567 redundancy removal entries, to bridge the gap between QS repositories and human gut microbiota. With the help of QSHGM, various communication-based microbial interactions can be searched and a QS communication network (QSCN) is further constructed and analysed for 818 human gut microbes. This work contributes to the establishment of the QSCN which may form one of the key knowledge maps of the human gut microbiota, supporting future applications such as new manipulations to synthetic microbiota and potential therapies to gut diseases.

Mechanism of biogenic amine synthesis of Enterococcus faecium isolated from Sanchun ham

Sanchuan ham, produced in Yunnan, China, is food with ethnic characteristics favored by consumers. However, it can contain biogenic amines such as tyramine that are harmful to health, and the synthesis mechanism of biogenic amines in Sanchuan ham is not clear. This study focuses on the regulation of biogenic amine synthesis by quorum sensing. We used high-performance liquid chromatography to detect the content of biogenic amine in different kinds of ham and found that the content of biogenic amine in Sanchuan ham was higher than that in others. Tyramine-producing strain isolated from Sanchuan ham was identified as Enterococcus faecium. By monitoring the growth and tyramine synthesis of Enterococcus faecium under cultured conditions, the results found that high temperature and low salt increased tyramine production by E. faecium. After seven exogenous amino acids were applied to E. faecium, only tyrosine could promote the production of tyramine in E. faecium, and tyramine could not be synthesized in E. faecium until a certain amount was reached, indicating the presence of microbial quorum sensing signal molecules in the synthesis of tyramine in E. faecium. Untargeted metabolomics analysis of the differential metabolites produced by E. faecium showed that the contents of some peptides, especially alanyl-leucine, were significantly increased. Further experiments with synthetic alanyl-leucine illustrated that alanyl-leucine activated the expression of tyrosine decarboxylase (tyrDC), thereby regulating the synthesis of tyramine by E. faecium. Alanyl-leucine acted as quorum sensing signal molecules for biogenic amine synthesis by E. faecium, which provided a theoretical basis for reducing biogenic amine accumulation in ham. It is beneficial to control the content of biogenic amines in ham in the future.

Antioxidant and Antimicrobial Peptides Derived from Food Proteins

Recently, the demand for food proteins in the market has increased due to a rise in degenerative illnesses that are associated with the excessive production of free radicals and the unwanted side effects of various drugs, for which researchers have suggested diets rich in bioactive compounds. Some of the functional compounds present in foods are antioxidant and antimicrobial peptides, which are used to produce foods that promote health and to reduce the consumption of antibiotics. These peptides have been obtained from various sources of proteins, such as foods and agri-food by-products, via enzymatic hydrolysis and microbial fermentation. Peptides with antioxidant properties exert effective metal ion (Fe2+/Cu2+) chelating activity and lipid peroxidation inhibition, which may lead to notably beneficial effects in promoting human health and food processing. Antimicrobial peptides are small oligo-peptides generally containing from 10 to 100 amino acids, with a net positive charge and an amphipathic structure; they are the most important components of the antibacterial defense of organisms at almost all levels of life-bacteria, fungi, plants, amphibians, insects, birds and mammals-and have been suggested as natural compounds that neutralize the toxicity of reactive oxygen species generated by antibiotics and the stress generated by various exogenous sources. This review discusses what antioxidant and antimicrobial peptides are, their source, production, some bioinformatics tools used for their obtainment, emerging technologies, and health benefits.

A bioanalytical screening method for Enterococcus faecalis RNPP-type quorum sensing peptides in murine feces

Background: Bacteria coordinate their behavior as a group via communication with their peers, known as 'quorum sensing'. Enterococcus faecalis employs quorum sensing via RNPP-peptides which were not yet reported to be present in mammalian biofluids. Results: Solid phase extraction of murine feces was performed, followed by ultra high performance liquid chromatography (UHPLC-MS/MS) in multiple reaction monitoring (MRM) mode (in total <90 min/sample) for the nine known RNPP peptides. Limits of detection ranged between 0.045 and 52 nM. Adequate identification criteria allowed detection of RNPP quorum sensing peptides in 2/20 wild-type murine feces samples (i.e., cAM373 and cOB1). Conclusion: A fit-for-purpose UHPLC-MS/MS method detected these RNPP peptides in wild-type murine feces samples.

Ribosomally synthesized peptides, foreground players in microbial interactions: recent developments and unanswered questions

It is currently well established that multicellular organisms live in tight association with complex communities of microorganisms including a large number of bacteria. These are immersed in complex interaction networks reflecting the relationships established between them and with host organisms; yet, little is known about the molecules and mechanisms involved in these mutual interactions. Ribosomally synthesized peptides, among which bacterial antimicrobial peptides called bacteriocins and microcins have been identified as contributing to host-microbe interplays, are either unmodified or post-translationally modified peptides. This review will unveil current knowledge on these ribosomal peptide-based natural products, their interplay with the host immune system, and their roles in microbial interactions and symbioses. It will include their major structural characteristics and post-translational modifications, the main rules of their maturation pathways, and the principal ecological functions they ensure (communication, signalization, competition), especially in symbiosis, taking select examples in various organisms. Finally, we address unanswered questions and provide a framework for deciphering big issues inspiring future directions in the field.

QSDB-a graphical Quorum Sensing Database

The human microbiome is largely shaped by the chemical interactions of its microbial members, which includes cross-talk via shared signals or quenching of the signalling of other species. Quorum sensing is a process that allows microbes to coordinate their behaviour in dependence of their population density and to adjust gene expression accordingly. We present the Quorum Sensing Database (QSDB), a comprehensive database of all published sensing and quenching relations between organisms and signalling molecules of the human microbiome, as well as an interactive web interface that allows browsing the database, provides graphical depictions of sensing mechanisms as Systems Biology Graphical Notation diagrams and links to other databases. Database URL: QSDB (Quorum Sensing DataBase) is freely available via an interactive web interface and as a downloadable csv file at http://qsdb.org.

Human TRPV1 and TRPA1 are receptors for bacterial quorum sensing molecules

In this study, we investigated the activation of TRPV1 and TRPA1 by N-acyl homoserine lactones, quorum sensing molecules produced by Gram-negative bacteria, and the inhibitory effect of TRPV1 and TRPA1 by autoinducing peptides, quorum sensing molecules produced by Gram-positive bacteria, using human embryonic kidney 293T cell lines stably expressing human TRPV1 and TRPA1, respectively. As a result, we found that some N-acyl homoserine lactones, such as N-octanoyl-L-homoserine lactone (C8-HSL), N-nonanoyl-L-homoserine lactone (C9-HSL) and N-decanoyl-L-homoserine lactone (C10-HSL) activated both TRPV1 and TRPA1. In addition, we clarified that some N-acyl homoserine lactones, for example, N-3-oxo-dodecanoyl-L-homoserine lactone (3-oxo-C12-HSL) only activated TRPV1, and N-acyl homoserine lactones having saturated short acyl chain, such as N-acetyl-L-homoserine lactone (C2-HSL) and N-butyryl-L-homoserine lactone (C4-HSL) only activated TRPA1, respectively. Furthermore, we found that an autoinducing peptide, simple linear peptide CHWPR, inhibited both TRPV1 and TRPA1, and peptide having thiolactone ring DICNAYF, thiolactone ring were formed between C3 to F7, strongly inhibited only the TRPV1. Although the specificity of TRPV1 and TRPA1 for quorum sensing molecules were different, these data suggest that both TRPV1 and TRPA1 would function as receptors for quorum sensing molecule produced by bacteria.

Natural antimicrobial peptides as a source of new antiviral agents

Current antiviral drugs are limited because of their adverse side effects and increased rate of resistance. In recent decades, much scientific effort has been invested in the discovery of new synthetic and natural compounds with promising antiviral properties. Among this new generation of compounds, antimicrobial peptides with antiviral activity have been described and are attracting attention due to their mechanism of action and biological properties. To understand the potential of antiviral peptides (AVPs), we analyse the antiviral activity of well-known AVP families isolated from different natural sources, discuss their physical-chemical properties, and demonstrate how AVP databases can guide us to design synthetic AVPs with better therapeutic properties. All considerations in this sphere of antiviral therapy clearly demonstrate the remarkable contribution that AVPs may make in conquering old as well as newly emerging viruses that plague humanity.

MolDiscovery: learning mass spectrometry fragmentation of small molecules

Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. The existing approaches are based on chemistry domain knowledge, and they fail to explain many of the peaks in mass spectra of small molecules. Here, we present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods.

PapRIV, a BV-2 microglial cell activating quorum sensing peptide

Quorum sensing peptides (QSPs) are bacterial peptides produced by Gram-positive bacteria to communicate with their peers in a cell-density dependent manner. These peptides do not only act as interbacterial communication signals, but can also have effects on the host. Compelling evidence demonstrates the presence of a gut-brain axis and more specifically, the role of the gut microbiota in microglial functioning. The aim of this study is to investigate microglial activating properties of a selected QSP (PapRIV) which is produced by Bacillus cereus species. PapRIV showed in vitro activating properties of BV-2 microglia cells and was able to cross the in vitro Caco-2 cell model and reach the brain. In vivo peptide presence was also demonstrated in mouse plasma. The peptide caused induction of IL-6, TNFα and ROS expression and increased the fraction of ameboid BV-2 microglia cells in an NF-κB dependent manner. Different metabolites were identified in serum, of which the main metabolite still remained active. PapRIV is thus able to cross the gastro-intestinal tract and the blood–brain barrier and shows in vitro activating properties in BV-2 microglia cells, hereby indicating a potential role of this quorum sensing peptide in gut-brain interaction.

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions

Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

Screening strategies for quorum sensing inhibitors in combating bacterial infections

Interference with quorum sensing (QS) represents an antivirulence strategy with a significant promise for the treatment of bacterial infections and a new approach to restoring antibiotic tolerance. Over the past two decades, a novel series of studies have reported that quorum quenching approaches and the discovery of quorum sensing inhibitors (QSIs) have a strong impact on the discovery of anti-infective drugs against various types of bacteria. The discovery of QSI was demonstrated to be an appropriate strategy to expand the anti-infective therapeutic approaches to complement classical antibiotics and antimicrobial agents. For the discovery of QSIs, diverse approaches exist and develop in-step with the scale of screening as well as specific QS systems. This review highlights the latest findings in strategies and methodologies for QSI screening, involving activity-based screening with bioassays, chemical methods to seek bacterial QS pathways for QSI discovery, virtual screening for QSI screening, and other potential tools for interpreting QS signaling, which are innovative routes for future efforts to discover additional QSIs to combat bacterial infections.

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Interference with QSrepresents a promising antivirulence strategy for the treatment of bacterial infections. The discovery ofQSIs was demonstrated as an appropriate strategy to expand the anti-infective therapeutic arsenal to complement classical antibiotics and antimicrobial agents.

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For the discovery of QSIs, diverse approaches exist and develop in-step with the scale of screening and targeted QS systems.

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Few previous reviews have summarized the strategies and approaches of QSI screening, whereas this review highlights the recent findings in QSI screening strategies and methodologies.

ITP-Pred: an interpretable method for predicting, therapeutic peptides with fused features low-dimension representation

The peptide therapeutics market is providing new opportunities for the biotechnology and pharmaceutical industries. Therefore, identifying therapeutic peptides and exploring their properties are important. Although several studies have proposed different machine learning methods to predict peptides as being therapeutic peptides, most do not explain the decision factors of model in detail. In this work, an Interpretable Therapeutic Peptide Prediction (ITP-Pred) model based on efficient feature fusion was developed. First, we proposed three kinds of feature descriptors based on sequence and physicochemical property encoded, namely amino acid composition (AAC), group AAC and coding autocorrelation, and concatenated them to obtain the feature representation of therapeutic peptide. Then, we input it into the CNN-Bi-directional Long Short-Term Memory (BiLSTM) model to automatically learn recognition of therapeutic peptides. The cross-validation and independent verification experiments results indicated that ITP-Pred has a higher prediction performance on the benchmark dataset than other comparison methods. Finally, we analyzed the output of the model from two aspects: sequence order and physical and chemical properties, mining important features as guidance for the design of better models that can complement existing methods.

Rich Repertoire of Quorum Sensing Protein Coding Sequences in CPR and DPANN Associated with Interspecies and Interkingdom Communication

The selection of predicted genes for interspecies communication within the CPR and DPANN genomes sheds some light onto the underlying mechanisms supporting their inferred symbiotic lifestyle. Also, considering the lack of core pathways such as the de novo synthesis of nucleotides or amino acids in the CPR and DPANN lineages, the persistence of these genes highlights how determinant social traits can be for the survival of some microorganisms. Finally, the considerable number of variants of QS proteins identified among the 69 CPR and DPANN phyla substantially expands our knowledge of prokaryotic communication across the tree of life and suggests that the multiplicity of “dialects” in the microbial world is probably larger than previously appreciated.

The bacterial candidate phyla radiation (CPR) and the archaeal DPANN superphylum are two novel lineages that have substantially expanded the tree of life due to their large phylogenetic diversity. Because of their ultrasmall size, reduced genome, and lack of core biosynthetic capabilities, most CPR and DPANN members are predicted to be sustained through their interactions with other species. How the few characterized CPR and DPANN symbionts achieve these critical interactions is, however, poorly understood. Here, we conducted an in silico analysis on 2,597 CPR/DPANN genomes to test whether these ultrasmall microorganisms might encode homologs of reference proteins involved in the synthesis and/or the detection of 26 different types of communication molecules (quorum sensing [QS] signals), since QS signals are well-known mediators of intra- and interorganismic relationships. We report the discovery of 5,693 variants of QS proteins distributed across 63 CPR and 6 DPANN phyla and associated with 14 distinct types of communication molecules, most of which were characterized as interspecies QS signals.

IMPORTANCE The selection of predicted genes for interspecies communication within the CPR and DPANN genomes sheds some light onto the underlying mechanisms supporting their inferred symbiotic lifestyle. Also, considering the lack of core pathways such as the de novo synthesis of nucleotides or amino acids in the CPR and DPANN lineages, the persistence of these genes highlights how determinant social traits can be for the survival of some microorganisms. Finally, the considerable number of variants of QS proteins identified among the 69 CPR and DPANN phyla substantially expands our knowledge of prokaryotic communication across the tree of life and suggests that the multiplicity of “dialects” in the microbial world is probably larger than previously appreciated.

Development and utilization of peptide-based quorum sensing modulators in Gram-positive bacteria

Quorum sensing (QS) is a mechanism by which bacteria regulate cell density-dependent group behaviors. Gram-positive bacteria generally rely on auto-inducing peptide (AIP)-based QS signaling to regulate their group behaviors. To develop synthetic modulators of these behaviors, the natural peptide needs to be identified and its structure-activity relationships (SARs) with its cognate receptor (either membrane-bound or cytosolic) need to be understood. SAR information allows for the rational design of peptides or peptide mimics with enhanced characteristics, which in turn can be utilized in studies to understand species-specific QS mechanisms and as lead scaffolds for the development of therapeutic candidates that target QS. In this review, we discuss recent work associated with the approaches used towards forwarding each of these steps in Gram-positive bacteria, with a focus on species that have received less attention.

Influence of Blood Collection Methods and Long-Term Plasma Storage on Quorum-Sensing Peptide Stability

Finding adequate biomarkers for rapid and accurate disease detection, prognosis, and therapy is increasingly important. Quorum-sensing peptides are herein a new emerging group, produced by bacteria, fungi, protozoa, and viruses, with blood being the most straightforward sample type to detect/quantitate them. However, detailed information about suitable blood sample collection methods and storage conditions for measuring these quorum-sensing peptides hampers further clinical research and development. Here, we first tested the time-dependent stability of a set of chemically diverse quorum-sensing peptides, spiked in blood at different temperatures (4, 21, and 37 °C) in four different ethylenediamine tetraacetic acid (EDTA)-containing plasma tubes (with different protein-stabilizing additives) over a period of up to 7.5 h. Next, we determined the storage stability of these quorum-sensing peptides in plasma at different temperatures (4, -35, and -80 °C). UPLC/MS-MS was used to selectively detect and quantify the spiked quorum-sensing peptides. The results of this study indicate that a cost-effective tube, designed for traditional proteomics and stored at 4 °C, is the preferred collection condition when quorum-sensing peptides need to be detected/quantified in human plasma. When the tubes are handled at room temperature (21 °C), a more specialized tube is required. Long-term storage of plasma samples, even under low-temperature conditions (-80 °C), indicates rapid degradation of certain quorum-sensing peptides.

Prevention of biofilm formation by quorum quenching

Quorum sensing (QS) is a mechanism that enables microbial communication. It is based on the constant secretion of signaling molecules to the environment. The main role of QS is the regulation of vital processes in the cell such as virulence factor production or biofilm formation. Due to still growing bacterial resistance to antibiotics that have been overused, it is necessary to search for alternative antimicrobial therapies. One of them is quorum quenching (QQ) that disrupts microbial communication. QQ-driving molecules can decrease or even completely inhibit the production of virulence factors (including biofilm formation). There are few QQ strategies that comprise the use of the structural analogues of QS receptor autoinductors (AI). They may be found in nature or be designed and synthesized via chemical engineering. Many of the characterized QQ molecules are enzymes with the ability to degrade signaling molecules. They can also impede cellular signaling cascades. There are different techniques used for testing QS/QQ, including chromatography-mass spectroscopy, bioluminescence, chemiluminescence, fluorescence, electrochemistry, and colorimetry. They all enable qualitative and quantitative measurements of QS/QQ molecules. This article gathers the information about the mechanisms of QS and QQ, and their effect on microbial biofilm formation. Basic methods used to study QS/QQ, as well as the medical and biotechnological applications of QQ, are also described. Basis research methods are also described as well as medical and biotechnological application.

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aBiofilm: a resource of anti-biofilm agents and their potential implications in targeting antibiotic drug resistance

Biofilms play an important role in the antibiotic drug resistance, which is threatening public health globally. Almost, all microbes mimic multicellular lifestyle to form biofilm by undergoing phenotypic changes to adapt adverse environmental conditions. Many anti-biofilm agents have been experimentally validated to disrupt the biofilms during last three decades. To organize this data, we developed the ‘aBiofilm’ resource (http://bioinfo.imtech.res.in/manojk/abiofilm/) that harbors a database, a predictor, and the data visualization modules. The database contains biological, chemical, and structural details of 5027 anti-biofilm agents (1720 unique) reported from 1988–2017. These agents target over 140 organisms including Gram-negative, Gram-positive bacteria, and fungus. They are mainly chemicals, peptides, phages, secondary metabolites, antibodies, nanoparticles and extracts. They show the diverse mode of actions by attacking mainly signaling molecules, biofilm matrix, genes, extracellular polymeric substances, and many more. The QSAR based predictor identifies the anti-biofilm potential of an unknown chemical with an accuracy of ∼80.00%. The data visualization section summarized the biofilm stages targeted (Circos plot); interaction maps (Cytoscape) and chemicals diversification (CheS-Mapper) of the agents. This comprehensive platform would help the researchers to understand the multilevel communication in the microbial consortium. It may aid in developing anti-biofilm therapeutics to deal with antibiotic drug resistance menace.